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Quantocracy’s Daily Wrap for 01/05/2024

This is a summary of links featured on Quantocracy on Friday, 01/05/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Why Do US Stocks Outperform EM and EAFE Regions? [Quantpedia]

    Investing in emerging markets (EM) or developed markets (DM) outside of the United States tends to follow cyclical trends. At times, it becomes popular and crowded to focus solely on U.S. stocks, while in other periods, the trend shifts to favor everything except U.S. equities. This inclination often relies on historical and past performance data, although it doesnt guarantee identical outcomes
  • Crowded Trades Increase Crash Risks [Alpha Architect]

    Arbitrageurs keep markets efficient by moving prices to reflect their fundamental values. However, anomalies can persist because of limits to arbitragethe costs and risks of shorting. The costs and risks of shorting, however, are not the only risks that arbitrageurs face. The publication of research on anomalies in asset pricing models has led to a dramatic increase in factor-based investment

Quantocracy’s Daily Wrap for 01/04/2024

This is a summary of links featured on Quantocracy on Thursday, 01/04/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Sketching the Option Backtester v2 (with Code downloadable for ALL readers) [Hanguk Quant]

    In the last post, we wrote code to test for the pnl of a system that continuously rebalances and shorts the atm straddle on index options. Sketching the Option Backtester (with Code downloadable for ALL readers) HangukQuant December 21, 2023 Sketching the Option Backtester (with Code downloadable for ALL readers) Read full story We just made a few notes – the code is rather inflexible in that

Quantocracy’s Daily Wrap for 01/02/2024

This is a summary of links featured on Quantocracy on Tuesday, 01/02/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • A Deep Dive into Volatility Targeting [Return Sources]

    In the world of trend following, the biggest, most longstanding debate is about whether or not to target a certain level of volatility on an ongoing basis. Listeners of the podcast Top Traders Unplugged will be very familiar with this debate. Unfortunately, some of the language surrounding this argument has become confusing and vague. When we say, volatility targeting, we need to be clear
  • Most popular posts 2023 [Eran Raviv]

    This blog is just a personal hobby. When Im extra busy as I was this year the blog is a front-line casualty. This is why 2023 saw a weaker posting stream. Nonetheless I am pleased with just over 30K visits this year, with an average of roughly one minute per visit (engagement time, whatever google-analytics means by that). This year I only provide the top two posts (rather than the usual 3).
  • Factor Olympics 2023 [Finominal]

    The performance of factors was unexciting and poor in 2023 Quality performed the best, low volatility the worst Low-risk and cheap stocks are currently highly correlated INTRODUCTION We present the performance of five well-known factors on an annual basis for the last 10 years. Specifically, we only present factors where academic research supports the existence of positive excess returns across

Quantocracy’s Daily Wrap for 01/01/2024

This is a summary of links featured on Quantocracy on Monday, 01/01/2024. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Weekend Effect in the Market Indices [Relative Value Arbitrage]

    The weekend (or Monday) effect in the stock market refers to the phenomenon where stock returns exhibit different patterns on Mondays compared to the rest of the week. Historically, there has been a tendency for stock prices to be lower on Mondays. Various theories attempt to explain the weekend effect, including investor behaviour, news over the weekend, and the impact of events occurring during

Quantocracy’s Daily Wrap for 12/30/2023

This is a summary of links featured on Quantocracy on Saturday, 12/30/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Quickly store 2,370,886 rows of historic options data with ArcticDB [PyQuant News]

    Over 1,200,000 options contracts trade daily. Storing options data for analysis has become something only professionals can do using sophisticated tools. One of the professionals recently open sourced their tools for lightening fast data storage and retrieval. ArcticDB is a DataFrame database that is used in production by the systematic trading company, Man Group. Its used for storage,
  • Tracking systematic default risk [SR SV]

    Systematic default risk is the probability of a critical share of the corporate sector defaulting simultaneously. It can be analyzed through a corporate default model that accounts for both firm-level and communal macro shocks. Point-in-time estimation of such a risk metric requires accounting data and market returns. Systematic default risk arises from the capital structures vulnerability and
  • The Financial Distress Puzzle [Alpha Architect]

    That riskier assets should command higher expected returns is the most basic of asset pricing theories. Clearly, financial distress is a risk characteristic, but it presents a puzzle, as there has not been a linear relationship between it and stock returns. For example, John Birge and Yi Zhang, authors of the April 2017 study Risk Factors That Explain Stock Returns: A Non-Linear Factor Pricing

Quantocracy’s Daily Wrap for 12/26/2023

This is a summary of links featured on Quantocracy on Tuesday, 12/26/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Differentiated Trend Following [Return Sources]

    Trend following boils down to one basic idea: buy when the price goes up, and sell when it goes down. Its implementation, though, could be much more complicated. There are a myriad methods and timeframes to choose from, and these methods and timeframes are by and large the dials that CTAs can turn in constructing their trend programs. One manager can focus on long term trend, another one medium
  • Easily cross-validate parameters to boost your trading strategy [PyQuant News]

    Trading strategies often rely on parameters. To enhance and effectively cross-validate these parameters can provide a competitive advantage in the market. However, reliable cross-validation strategies can lead to look-ahead bias and other pitfalls that can lead to overestimating a strategys performance. In todays newsletter, well use VectorBT PRO to easily implement a variety of
  • Are stock returns predictable at different points in time? [Alpha Architect]

    The question of whether stock returns are predictable is of long-standing interest to both academics and investment practitioners. Commonly accepted investment strategies, for example, will behave quite differently in the presence of stock return predictability. The research literature is unclear on the answer and suggests that return predictability, if it exists, will be difficult to exploit on
  • Momentum Everywhere, Including Equity Options [Alpha Architect]

    Because of the strong evidence, momentum continues to receive much attention from researchers. Out of the hundreds of exhibits in the factor zoo, one of just five equity factors that met all the criteria (persistent, pervasive, robust, implementable, and intuitive) Andrew Berkin and I established in our book Your Complete Guide to Factor-Based Investing was momentum (both cross-sectional

Quantocracy’s Daily Wrap for 12/21/2023

This is a summary of links featured on Quantocracy on Thursday, 12/21/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • 2023 Rally – How Strong Is It? [Alvarez Quant Trading]

    This end of year rally which started on October 2023 has been strong. My trading buddy and I started wondering how this compares to the past. Is this a normal strong rally or an abnormally strong one? Determining this is always tough because it depends on the indicators you use. Because of that, I tried lots of them. This will be a post short on words but with lots of tables. Where are
  • Judging the Quality of Indicators [Dekalog Blog]

    In my previous post I said I was trying to develop new indicators from the results of my new PositionBook optimisation routine. In doing so, I need to have a methodology for judging the quality of the indicator(s). In the past I created a Data-Snooping-Tests-GitHub which contains some tests for statistical significance testing and which, of course, can be used on these new indicators.
  • Research Review | 21 DEC 2023 | Portfolio Design & Risk Factors [Capital Spectator]

    Factor Zoo (.zip) Alexander Swade (Lancaster University) et al. October 2023 The number of factors allegedly driving the cross-section of stock returns has grown steadily over time. We explore how much this factor zoo can be compressed, focusing on explaining the available alpha rather than the covariance matrix of factor returns. Our findings indicate that about 15 factors are enough to

Quantocracy’s Daily Wrap for 12/20/2023

This is a summary of links featured on Quantocracy on Wednesday, 12/20/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Trend Following VS. Volatility Capping: Two Kinds of Insurance [Return Sources]

    An equity investor can purchase two kinds of financial insurance. The first, more straightforward kind, is a put option. This contract simply pays off when the S&P 500 (which well use as our stand-in for equity) goes down. In other words, its like any other insurance contract. It protects you against losses from a specific event by rising in value when the event occurs.

Quantocracy’s Daily Wrap for 12/19/2023

This is a summary of links featured on Quantocracy on Tuesday, 12/19/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Beyond Modified Value-at-Risk: Application of Gaussian Mixtures to Value-at-Risk [Portfolio Optimizer]

    In a previous post, I described a parametric approach to computing Value-at-Risk (VaR) – called modified VaR12 – that adjusts Gaussian VaR for asymmetry and fat tails present in financial asset returns3 thanks to the usage of a CornishFisher expansion. Modified VaR, when properly used4, provides accurate estimates of the VaR for a wide range of non-normal portfolio return distributions.
  • Can Machine Learning help to select mutual funds with positive alpha? [Alpha Architect]

    The study emphasizes the importance of integrating machine learning with other tools for investment managers, pension-plan administrators, financial advisors, and independent analysts to help investors select active mutual funds with positive alpha. It also highlights the significance of fund characteristics in predicting alpha, even when portfolio holdings are not disclosed.

Quantocracy’s Daily Wrap for 12/18/2023

This is a summary of links featured on Quantocracy on Monday, 12/18/2023. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Spearman’s rank correlation of technical indicators [Grzegorz Link]

    RSI, MACD, Stochastic, ROC, CCI, %b – technical indicators come in many shapes and sizes.[1] Their names suggest something very technical at play. Maybe even scientific. Yet, they are a polarizing tool. They generate strong, opposing opinions. Some traders value them with near religious zeal, while others despise them as a useless mix of witchcraft and salesmanship, and quite simply, scams. Or at
  • Directional Change in Trading: Indicators, Python Coding, and HMM Strategies [Quant Insti]

    Usually, regime detection is made with an HMM estimation over price returns or price return volatility. However, Chen and Tsang (2021) propose to use the Directional Change indicators as input for a HMM to detect regime shifts. They show that the HMM applied to the Directional Change indicators detects regime shifts better than with an HMM applied to price return volatility. Here we apply
  • How to ingest premium market data with Zipline Reloaded [PyQuant News]

    This article explains how to build the two Python scripts you need to use premium data to create a custom data bundle using Zipline Reloaded. Step 1: Subscribe to premium data By now you should already have an account with Nasdaq Data Link. If not, head over to https://data.nasdaq.com and set one up. Youre looking for QuoteMedia End of Day US Stock Prices. This product offers end-of-day prices,